Prof. Ujjwal Das, IIM Udaipur

Missing causes of failure are quite frequent in survival and
reliability studies. Surprisingly for interval censored data, this
problem has not been investigated much, albeit in
lifetime studies such data occur frequently. In this article, interval
censored competing risks data are analyzed when some of the causes of
failure are missing. The proposed technique uses vertical modeling, an
approach that utilizes the data to extract information to the maximum
possible extent, especially when some causes of failure are missing.
The maximum likelihood estimates of the model parameters are obtained.
Through a Monte Carlo simulation study, the performance of the point
and interval estimators are assessed. It is observed through the
simulation study that the proposed analysis performs better than the
complete case analysis. Such analysis is particularly relevant for
smaller sample sizes, as carrying out a complete case analysis in
those cases may have a significant impact on the inferential
procedures. Through Monte Carlo simulations, the effect of a possible
model misspecification is also assessed on the cumulative incidence
function which is an important statistic in the framework of competing
risks. The proposed method has been illustrated on a real data set.